Name
Title | ||
|---|---|---|
Neural-network-based distributed adaptive asymptotically consensus tracking control for nonlinear multiagent systems with input quantization and actuator faults. |
Abstract | ||
|---|---|---|
This paper investigates the consensus asymptotic convergence problem for a class of nth-order strict-feedback multiagent systems, which include the input quantization, actuator faults, unknown nonlinear functions and directed communication topology. Because the upper bounds of the time-varying stuck faults and external disturbance are commonly difficult to accurately determine, it is also assumed that these upper bounds are unknown in this paper. First, a group of first-order filters are designed to estimate the bounds of the reference signal for each agent. Second, smooth functions are introduced to compensate the effect of quantization and bounded stuck faults. Meanwhile, a new back-stepping method is used to propose an intermediate control law and an adaptive design procedure, and the final distributed control protocols are established. All closed-loop signals are uniformly bounded, and the tracking errors asymptotically converge to zero. Finally, a practical example simulation is provided to demonstrate the effectiveness of the proposed scheme. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.neucom.2019.04.018 | Neurocomputing |
Keywords | Field | DocType |
Distributed consensus control,Neural networks,Input quantization,Actuator fault,Consensus asymptotic convergence | Nonlinear system,Pattern recognition,Control theory,Uniform boundedness,Convergence problem,Multi-agent system,Artificial intelligence,Quantization (signal processing),Artificial neural network,Mathematics,Actuator,Bounded function | Journal |
Volume | ISSN | Citations |
349 | 0925-2312 | 3 |
PageRank | References | Authors |
0.37 | 0 | 5 |